Welcome to Adwait's Homepage

Adwait Jog, Ph.D. (Penn State)    Assistant Professor    Department of Computer Science    The College of William and Mary    McGlothlin-Street Hall 111    Williamsburg, VA 23187     adwait@cs.wm.edu, Google Scholar Profile

About

I am an assistant professor of computer science at The College of William and Mary (W&M) (US News Rankings) in Virginia, USA. At W&M, I am leading the Insight Computer Architecture Lab with the goal to generate insights that would enable more energy-efficient, reliable, and secure computing system architecture.

I am looking for self-motivated students to work with. Current topics of interest include (but not limited to): GPU Memory System, Approximate GPU Computing, GPU Reliability and Security, and Non-traditional Architectures (e.g., Automata Processor, Quantum Architectures).

If you are interested, email me with your CV. If you are not currently at W&M, also apply here.

Recent News

• Our newly formed Insight Computer Architecture Lab will showcase two papers at HPCA 2018.

  • We developed new memory bandwidth management techniques for efficient and fair multi-application execution in GPUs.

  • We developed new randomized memory coalescing mechanisms for efficiently balancing performance and security in GPUs.

• Invited to be on the Program Committee of ISPASS 2018. Submit your best work!

• Co-organized a workshop on minimizing data movement (Min-Move 2017) co-located with PACT 2017.

• As a part of outreach, I gave a talk on Career Opportunities in CS to middle school students attending Focusing on the Future (FOF) conference organized by School of Education, W&M.

• Received NSF SHF Award 2017, PI (co-PI: Evgenia Smirni). Thanks NSF!

• Received NSF CRII Award 2017, sole PI. Thanks NSF!

• Invited to be on the Program Committee of HPCA 2018. Submit your best work!

• Papers accepted in HPCA 2017, VLSID 2017, and ISVLSI 2017

• Papers accepted in IISWC 2016, PACT (2) 2016, MICRO 2016, and SIGMETRICS 2016.

• Served on the Program Committees of MICRO 2017 (ERC), DSN 2017, HPDC 2017, GPGPU 2017, and NAS 2017.

Current Students

Doctoral:

• Mohamed Ibrahim (Ph.D., Jan 2016 –)

• Haonan Wang (Ph.D., Jan 2016 –)

• Gurunath Kadam (Ph.D., Aug 2016 –)

• Hongyuan Liu (Ph.D., Jan 2017 –)

Alumni

Masters:

• Corey Ames, Rongdong Chai, (M.S. 2017)

• Fan Luo (M.S. 2016. First Employment: Ph.D. student at University of Arizona)

Undergraduate:

• Colin Weinshenker (Honor's Thesis 2017, winner of Park Award, First Employment: Intel)

• Robert Risque (Senior's Thesis 2016)

Selected Publications (complete list)

• (HPCA 2018) Efficient and Fair Multi-programming in GPUs via Pattern-based Effective Bandwidth Management

• (HPCA 2018) RCoal: Mitigating GPU Timing Attack via Subwarp-based Randomized Coalescing Techniques

• (HPCA 2017) Controlled Kernel Launch for Dynamic Parallelism in GPUs

• (MICRO 2016) Zorua: A Holistic Approach to Resource Virtualization in GPUs

• (PACT 2016) Scheduling Techniques for GPU Architectures with Processing-In-Memory Capabilities

• (PACT 2016) μC-States: Fine-grained GPU Datapath Power Management

• (SIGMETRICS 2016) Exploiting Core Criticality for Enhanced GPU Performance

• (ISCA 2015) A Case for Core-Assisted Bottleneck Acceleration in GPUs: Enabling Flexible Data Compression with Assist Warps

• (MICRO 2014) Managing GPU Concurrency in Heterogeneous Architectures

• (PACT 2014) Trading Cache Hit Rate for Memory Performance

• (PACT 2013) Neither More Nor Less: Optimizing Thread-level Parallelism for GPGPUs

• (ISCA 2013) Orchestrated Scheduling and Prefetching for GPGPUs

• (ASPLOS 2013) OWL: Cooperative Thread Array Aware Scheduling Techniques for Improving GPGPU performance

• (DAC 2012) Cache Revive: Architecting Volatile STT-RAM Caches for Enhanced Performance in CMPs